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Statistical postprocessing of different variables for airports in Spain using machine learning
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dc.contributor.authorQuintero Plaza, Davides_ES
dc.contributor.authorGarcía-Moya, José Antonio-
dc.identifier.citationAdvances in Meteorology. 2019, p. 1-14es_ES
dc.description.abstractThe results of a deterministic calibration for the nonhydrostatic convection-permitting LAM-EPS AEMET-γSREPS are shown. LAM-EPS AEMET-γSREPS is a multiboundary condition, multimodel ensemble forecast system developed for Spain. Machine learning tools are used to calibrate the members of the ensemble. Machine learning (hereafter ML) has been considerably successful in many problems, and recent research suggests that meteorology and climatology are not an exception. These machine learning tools range from classical statistical methods to contemporary successful and powerful methods such as kernels and neural networks. The calibration has been done for airports located in many regions of Spain, representing different climatic conditions. The variables to be calibrated are the 2-meter temperature, the 10-meter wind speed, and the precipitation in 24 hours. Classical statistical methods perform very well with the temperature and the wind speed; the precipitation is a subtler case: it seems there is not a general rule, and for each point, a decision has to be taken of what method (if any) improves the direct output of the model, but even recognizing this, a slight improvement can be shown with ML methods for the precipitation.es_ES
dc.description.sponsorshipThe authors thank the Spanish weather service, AEMET, for its funding and support, both the headquarters office and the local office of the Canary Islands.es_ES
dc.rightsLicencia CC: Reconocimiento CC BYes_ES
dc.subjectMachine learninges_ES
dc.subjectWind speedes_ES
dc.titleStatistical postprocessing of different variables for airports in Spain using machine learninges_ES
Appears in Collections:Artículos científicos 2019-2022

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